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Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

BACKGROUND: Defining the tumor immune microenvironment (TIME) of patients using transcriptome analysis is gaining more popularity. Here, we examined and discussed the pros and cons of using RNA sequencing for fresh frozen samples and targeted gene expression immune profiles (NanoString) for formalin-fixed, paraffin-embedded (FFPE) samples to characterize the TIME of ependymoma samples.RESULTS: Our results showed a stable expression of the 40 housekeeping genes throughout all samples. The Pearson correlation of the endogenous genes was high. To define the TIME, we first checked the expression of the PTPRC gene, known as CD45, and found it was above the detection limit in all samples by both techniques. T cells were identified consistently using the two types of data. In addition, both techniques showed that the immune landscape was heterogeneous in the 6 ependymoma samples used for this study.CONCLUSIONS: The low-abundant genes were detected in higher quantities using the NanoString technique, even when FFPE samples were used. RNA sequencing is better suited for biomarker discovery, fusion gene detection, and getting a broader overview of the TIME. The technique that was used to measure the samples had a considerable effect on the type of immune cells that were identified. The limited number of tumor-infiltrating immune cells compared to the high density of tumor cells in ependymoma can limit the sensitivity of RNA expression techniques regarding the identification of the infiltrating immune cells.

Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

Background: Pancreatic ductal adenocarcinoma (PDAC) is notorious for its poor prognosis even after curative resection. Responses to immunotherapy are rare and related to inadequate T-cell priming. We previously demonstrated the potency of allogeneic lysate-dendritic cell (DC) vaccination in a preclinical model. Here we translate this concept to patients. Methods: In this phase I study, patients with resected PDAC were included when they demonstrated no radiologic signs of recurrence after standard-of-care treatment. Allogeneic tumour lysate-loaded autologous monocyte-derived DCs were injected at weeks 0, 2, 4 and at months 3 and 6. Objectives are feasibility, safety and immunogenicity of allogeneic tumour-DCs. The presence of tumour antigens shared between the vaccine and patient tumours was investigated. Immunological analyses were performed on peripheral blood, skin and tumour. Results: Ten patients were included. DC production and administration were successful. All patients experienced a grade 1 injection-site and infusion-related reaction. Two patients experienced a grade 2 fever and 1 patient experienced a grade 3 dyspnoea. No vaccine-related serious adverse events were observed. Shared tumour antigens were found between the vaccine and patient tumours. All evaluated patients displayed a vaccine-induced response indicated by increased frequencies of Ki67+ and activated PD-1+ circulating T-cells. In addition, treatment-induced T-cell reactivity to autologous tumour of study patients was detected. Seven out of ten patients have not experienced disease recurrence or progression at a median follow-up of 25 months (15–32 months). Conclusion: Allogeneic tumour lysate-DC treatment is feasible, safe and induces immune reactivity to PDAC expressed antigens.

Background: Long-read sequencing can be applied to generate very long contigs and even completely assembled genomes at relatively low cost and with minimal sample preparation. As a result, long-read sequencing platforms are becoming more popular. In this respect, the Oxford Nanopore Technologies–based long-read sequencing “nanopore” platform is becoming a widely used tool with a broad range of applications and end-users. However, the need to explore and manipulate the complex data generated by long-read sequencing platforms necessitates accompanying specialized bioinformatics platforms and tools to process the long-read data correctly. Importantly, such tools should additionally help democratize bioinformatics

Monitoring changes in the immune profile in blood samples can help identifying changes in tumor biology and therapy responsiveness over time. Immune-related gene expression profiles offer a highly reproducible method to monitor changes of the immune system. However, measuring gene expression profiles in whole blood samples can be complicated because of the high protein and enzyme abundancy that affect the stability and quality of the RNA. Peripheral blood mononuclear cells (PBMCs) are one the most commonly used source for immune cell RNA extraction, though, this method does not reflect all components of the peripheral blood. The aim of this study was to determine the differences in immune-related gene expression between RNA isolated from stabilized whole blood and RNA isolated from PBMCs. Whole blood samples from 12 pancreatic cancer patients were collected before and after chemotherapy (n = 24). Blood samples were collected in both EDTA tubes, and Tempus tubes containing an RNA stabilizer (total n = 48). PBMCs were isolated from EDTA samples using Ficoll and were snap frozen. Subsequently, immune-related gene expression was profiled using the PanCancer Immune Profiling Panel of NanoString technology. Gene expression profiles of PBMCs were compared to that of Tempus tubes using the Advanced Analysis module of nSolver software. Both types of samples provided good quality RNA and gene expression measurements. However, RNA isolated from Tempus tubes resulted in significantly higher gene counts than PBMCs; 107/730 genes were exclusively detected in Tempus samples, while under the detection limit in PBMCs. In addition, 192/730 genes showed significantly higher gene counts in Tempus samples, 157/730 genes showed higher gene counts in PBMCs. Thus, RNA isolated from whole blood stabilizing blood tubes, such as Tempus tubes, enable higher gene counts and more comprehensive measurements of gene expression profiles compared to RNA isolated from PBMCs.

Integer division is an important arithmetic operation on microprocessors. To derive integer division algorithms we present an unconvential approach: a derivation technique in a calculational style, that guarantees that the derived algorithms are correct. Four different algorithms are derived using this method: restoring division, non-restoring divsion, radix-4 division and division by multiplication. We translate these to descriptions into combinatorial circuits, expressed in Verilog code. Then the circuits are compiled on a Spartan-3 Generation FPGA. At the end, we compare the propagation delays and area requirements for these circuits. We show that the division by multiplication is much faster than the other methods, however it only works for 18 bit integers., Integer division is an important arithmetic operation on microprocessors. To derive integer division algorithms we present an unconvential approach: a derivation technique in a calculational style, that guarantees that the derived algorithms are correct. Four different algorithms are derived using this method: restoring division, non-restoring divsion, radix-4 division and division by multiplication. We translate these to descriptions into combinatorial circuits, expressed in Verilog code. Then the circuits are compiled on a Spartan-3 Generation FPGA. At the end, we compare the propagation delays and area requirements for these circuits. We show that the division by multiplication is much faster than the other methods, however it only works for 18 bit integers.